Techniques for Image Segmentation

1. U-Net

• Architecture: U-Net is a fully convolutional network designed for biomedical image segmentation.
• Structure: It consists of a contracting path to capture context and a symmetric expanding path for precise localization.
• Features: U-Net is efficient for small datasets, uses skip connections to preserve spatial information, and is effective for biomedical and satellite image segmentation tasks.

2. SegNet

• Architecture: SegNet is an encoder-decoder network designed for pixel-wise semantic segmentation.
• Structure: It uses a VGG-like encoder for feature extraction and a mirrored decoder for pixel-wise prediction.
• Features: SegNet uses max-pooling indices for upsampling, reducing computation compared to fully convolutional networks, and is suitable for real-time applications.

3. Mask R-CNN

• Architecture: Mask R-CNN extends Faster R-CNN by adding a branch for predicting segmentation masks alongside bounding box detection.
• Structure: It integrates a Region Proposal Network (RPN) for generating region proposals and a Mask Head for pixel-level segmentation.
• Features: Mask R-CNN achieves state-of-the-art results in instance segmentation tasks by enabling precise object segmentation and detection in complex scenes.

4. DeepLab

• Architecture: DeepLab is a series of convolutional neural networks designed for semantic image segmentation.
• Structure: It includes atrous convolution (dilated convolution) to capture multi-scale context and reduce downsampling artifacts.
• Features: DeepLab achieves high-resolution segmentation and is effective for tasks requiring detailed understanding of object boundaries and textures.

5. FCN (Fully Convolutional Network)

• Architecture: FCN is a pioneering method for pixel-wise semantic segmentation using only convolutional layers.
• Structure: It replaces fully connected layers with convolutional layers for end-to-end pixel-wise prediction.
• Features: FCN is versatile, capable of handling images of arbitrary size, and is used in various applications, including medical imaging and satellite image analysis.

Applications

• Medical Imaging: U-Net and DeepLab are widely used for organ segmentation and tumor detection.
• Autonomous Driving: SegNet and Mask R-CNN are employed for road scene understanding and object detection.
• Satellite Imaging: FCN and U-Net are applied for land cover mapping and environmental monitoring.